Comparison of Pixel and Object Based Approaches Using Landsat Data for Land Use and Land Cover Classification in Coastal Zone of Medan, Sumatera
Bibliographic record
Abstract
As an archipelagic country, Indonesia has the second longest coastal areas in the world after Canada. Coastal zone has dynamic characteristics. There are many changes here because of an unique ecology systems, sedimentation as well as human activities. Because of the coastal zone dynamics especially land use and land cover therefore it is important to identify them by using remote sensing technology. This paper discuss the application of Landsat satellite remote sensing image to classify land use and land cover by using object based classification approach in part of coastal zone of Medan, North Sumatera, Indonesia. Conventional classification methods use per pixel approaches that rely only on the spectral information or colours contained in the image. Otherwise object based classification approach firstly the image is segmented into objects. In subsequent steps, segments are merged based on their level of similarity. The user uses a scale parameter which indirectly controls the size of objects by specifying how much heterogeneity is allowed within each. User-defined color and shape parameters can also be set to change the relative weighting of reflectance and shape in defining segments. The methodology is consisted of satellite data acquisition, existing topographic map and statistical data collection, rectification of Landsat image, classification of land use and land cover using maximum likelihood algorithm and object based approach. Finally, the result shows that use of object based classification system provides reliable classification result than using traditional method such maximum likelihood classification system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".